Instructions to use dilip025/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dilip025/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dilip025/dummy-model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dilip025/dummy-model") model = AutoModelForMaskedLM.from_pretrained("dilip025/dummy-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenized_train_datasetii1.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.05 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/22d7060d4e65960b27686cb03ee4daa8f967b8f4/tokenized_train_datasetii1.pt
- Command line
-
hf download hf://dilip025/dummy-model@22d7060d4e65960b27686cb03ee4daa8f967b8f4/tokenized_train_datasetii1.pt
-
curl -L -o tokenized_train_datasetii1.pt https://huggingface.co/dilip025/dummy-model/resolve/22d7060d4e65960b27686cb03ee4daa8f967b8f4/tokenized_train_datasetii1.pt
4.05 GB
- Xet hash:
- de95c4174633ae858cbfcec8171ee716bbe777d240bf6bbb594c5a509406186c
- Size of remote file:
- 4.05 GB
- SHA256:
- b8095b6bb93b806bc457c7249a5b43f98d624940bb4493f9a21b2d25955bcd16
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